DeepXPalm: Tilt and Position Rendering using Palm-worn Haptic Display and CNN-based Tactile Pattern Recognition

TL;DR

This paper introduces a telemanipulation system with a palm-worn haptic device and CNN-based tactile recognition to improve tilt and position perception of deformable objects, significantly enhancing user accuracy.

Contribution

It presents a novel CNN-based method for recognizing tilt and position in deformable object manipulation, integrated with a multi-contact haptic feedback system.

Findings

Tilt and position recognition improved from 9.67% to 82.5%.

Enhanced tactile feedback improves user perception during telemanipulation.

System effectively aids manipulation of deformable objects.

Abstract

Telemanipulation of deformable objects requires high precision and dexterity from the users, which can be increased by kinesthetic and tactile feedback. However, the object shape can change dynamically, causing ambiguous perception of its alignment and hence errors in the robot positioning. Therefore, the tilt angle and position classification problem has to be solved to present a clear tactile pattern to the user. This work presents a telemanipulation system for plastic pipettes consisting of a multi-contact haptic device LinkGlide to deliver haptic feedback at the users' palm and two tactile sensors array embedded in the 2-finger Robotiq gripper. We propose a novel approach based on Convolutional Neural Networks (CNN) to detect the tilt and position while grasping deformable objects. The CNN generates a mask based on recognized tilt and position data to render further multi-contact tactile stimuli provided to the user during the telemanipulation. The study has shown that using the CNN algorithm and the preset mask, tilt, and position recognition by users is increased from 9.67% using the direct data to 82.5%.